Nuclear Medicine

Data-scarce transfer learning fusion for positron emission tomography and computed tomography guided lung biopsy.

OBJECTIVES: We developed a transfer learning-based multimodal fusion deep learning model integrating positron emission tomography/computed tomography (PET/CT) and procedural CT to support CT-guided percutaneous lung lesion biopsy, aiming to overcome PET/CT annotation scarcity and provide automated lesion segmentation and needle trajectory recommendations. METHODS: In this single-center retrospecti...

Wasserstein Deep Convolutional GAN With Growth Optimizer for Multi-Modal Feature Extraction in Cardiovascular Diagnosis.

BACKGROUND: Cardiovascular disease (CVD) diagnosis using multimodal health care data remains a major challenge due to the heterogeneity of clinical and imaging information, along with the instability of generative deep learning models trained on limited medical data sets. METHODS: This research proposes a hybrid multimodal framework termed WDCGAN-COA-CD-MD for integrating Electronic Health Records...

Rhodamine-benzothiazole dyad: AIE-active probe with solid-state optoelectronic properties, selective sensing of 1,3-diaminopropane and hydrazine detection guided by DFT and machine learning.

The widespread release of organic amines from industrial waste and food spoilage poses a significant environmental and food safety concern. Herein, to...

Future Directions for [18F]FDG PET in Central Nervous System Diseases.

[18F]FDG PET is entering a new phase shaped by changes in representation, validation, and clinical integration. Beyond regional interpretation, networ...

Diagnostic performance of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules.

PURPOSE: To evaluate the diagnostic value of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for different...

Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Transarterial radioembolization (TARE) is increasingly used for patients with hepatocellular carcinoma (HCC) across Barcelon...

Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.

OBJECTIVE: To evaluate a machine learning (ML) model that integrates clinical data and 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG)-PET radiomic features...

Carotid inflammation on FDG-PET is associated with lower cognitive performance in people with treated HIV infection.

OBJECTIVES: Carotid atherosclerosis is an established risk factor for cognitive impairment. FDG-PET detects subtle inflammatory changes in the arteria...

Semiquantitative [¹²³I]FP-CIT SPECT metrics combined with machine learning improve clinical differentiation of Parkinson's disease and atypical parkinsonian syndrome.

PURPOSE: To evaluate whether semiquantitative striatal [¹²³I]FP-CIT SPECT-derived metrics improve clinical differentiation of degenerative parkinsonis...

Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.

BACKGROUND: Vessels encapsulating tumor clusters (VETCs), a CD34-positive vascular pattern in hepatocellular carcinoma (HCC), are linked to aggressive...

AI-augmented thyroid scintigraphy for robust classification of disease.

Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imbalanced d...

Innovation districts and transformative workspaces: A scoping review of AI-pet robots companionship for aging employees balancing productivity and wellbeing.

BACKGROUND: Considering the future of work and an aging workforce, emerging technologies such as artificial intelligence (AI) and robots are promising...

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Positron Emission Tomography (PET) diagnostic precision is often compromised by low spatial resolution. Deep learning restoration models tend to sacri...

Optimization of hypovascular liver lesion detectability in dual-energy CT using deep learning image reconstruction: a phantom study for potential iodine dose reduction.

OBJECTIVE: To determine the optimal low-keV level using deep learning image reconstruction (DLIR) that maximizes lesion detectability, and to assess t...

Machine learning-guided discovery of poly(ethylene terephthalate)-binding modules to enhance durable whole-cell degradation.

The enzymatic degradation of poly(ethylene terephthalate) (PET) offers a sustainable route for plastic recycling but is often hindered by limited enzy...

Automatic lesion segmentation in ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT: added value of PET-guided SPECT in a bicentric study.

BACKGROUND: Manual segmentation of prostate cancer metastases on PSMA PET/CT and SPECT/CT is time-consuming and poorly scalable, particularly in highl...

PET/MR: history, current state, and technology.

Integrated PET/MR combines the molecular sensitivity of PET with the superior soft-tissue contrast and multiparametric capabilities of MRI, enabling s...

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

Simultaneous dual-tracer PET provides more comprehensive information for clinical diagnosis than standard PET imaging, but separating the hybrid dual-...

Beyond the tumor: Recurrence-prone radiomics for prognostication in negative PSMA PET/CT scans of prostate cancer.

Prostate-specific membrane antigen (PSMA) PET/CT is routinely used to restage prostate cancer (PCa) in patients with biochemical recurrence (BCR), yet...

Secondary prevention of recurrent ischemic stroke: from guidelines to precision medicine.

Recurrent ischemic stroke remains a major global health challenge, accounting for substantial disability and mortality despite advances in acute manag...